Deep learning techniques for detecting freezing of gait episodes in Parkinson’s disease using wearable sensors
Freezing of Gait (FoG) is a disabling motor symptom that characterizes Parkinson’s Disease (PD) patients and significantly affects their mobility and quality of life. The paper presents a novel hybrid deep learning framework for the detection of FoG episodes using wearable sensors. The methodology c...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-05-01
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| Series: | Frontiers in Physiology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fphys.2025.1581699/full |
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| author | Mosleh Hmoud Al-Adhaileh Mosleh Hmoud Al-Adhaileh Asim Wadood Theyazn H. H. Aldhyani Theyazn H. H. Aldhyani Safeer Khan M. Irfan Uddin Abdullah H. Al-Nefaie Abdullah H. Al-Nefaie |
| author_facet | Mosleh Hmoud Al-Adhaileh Mosleh Hmoud Al-Adhaileh Asim Wadood Theyazn H. H. Aldhyani Theyazn H. H. Aldhyani Safeer Khan M. Irfan Uddin Abdullah H. Al-Nefaie Abdullah H. Al-Nefaie |
| author_sort | Mosleh Hmoud Al-Adhaileh |
| collection | DOAJ |
| description | Freezing of Gait (FoG) is a disabling motor symptom that characterizes Parkinson’s Disease (PD) patients and significantly affects their mobility and quality of life. The paper presents a novel hybrid deep learning framework for the detection of FoG episodes using wearable sensors. The methodology combines CNNs for spatial feature extraction, BiLSTM networks for temporal modeling, and an attention mechanism to enhance interpretability and focus on critical gait features. The approach leverages multimodal datasets, including tDCS FOG, DeFOG, Daily Living, and Hantao’s Multimodal, to ensure robustness and generalizability. The proposed model deals with sensor noise, inter-subject variability, and data imbalance through comprehensive preprocessing techniques such as sensor fusion, normalization, and data augmentation. The proposed model achieved an average accuracy of 92.5%, F1-score of 89.3%, and AUC of 0.91, outperforming state-of-the-art methods. Post-training quantization and pruning enabled deployment on edge devices such as Raspberry Pi and Coral TPU, achieving inference latency under 350 ms. Ablation studies show the critical contribution of key architectural components to the model’s effectiveness. Optimized to be deployed real-time, it is a potentially promising solution that can help correctly detect FoG, thereby achieving better clinical monitoring and improving patients’ outcomes in a controlled as well as real world. |
| format | Article |
| id | doaj-art-967967f7fd5048aca9bc3cbf8a45477a |
| institution | DOAJ |
| issn | 1664-042X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Physiology |
| spelling | doaj-art-967967f7fd5048aca9bc3cbf8a45477a2025-08-20T03:14:20ZengFrontiers Media S.A.Frontiers in Physiology1664-042X2025-05-011610.3389/fphys.2025.15816991581699Deep learning techniques for detecting freezing of gait episodes in Parkinson’s disease using wearable sensorsMosleh Hmoud Al-Adhaileh0Mosleh Hmoud Al-Adhaileh1Asim Wadood2Theyazn H. H. Aldhyani3Theyazn H. H. Aldhyani4Safeer Khan5M. Irfan Uddin6Abdullah H. Al-Nefaie7Abdullah H. Al-Nefaie8King Salman Center for Disability Research, Riyadh, Saudi ArabiaDeanship of E-Learning and Information Technology, King Faisal University, Al-Ahsa, Saudi ArabiaInstitute of Computing, Kohat University of Science and Technology, Kohat, PakistanKing Salman Center for Disability Research, Riyadh, Saudi ArabiaApplied College in Abqaiq, King Faisal University, Al-Ahsa, Saudi ArabiaInstitute of Computing, Kohat University of Science and Technology, Kohat, PakistanInstitute of Computing, Kohat University of Science and Technology, Kohat, PakistanKing Salman Center for Disability Research, Riyadh, Saudi ArabiaDepartment of Quantitative Methods, School of Business, King Faisal University, Al-Ahsa, Saudi ArabiaFreezing of Gait (FoG) is a disabling motor symptom that characterizes Parkinson’s Disease (PD) patients and significantly affects their mobility and quality of life. The paper presents a novel hybrid deep learning framework for the detection of FoG episodes using wearable sensors. The methodology combines CNNs for spatial feature extraction, BiLSTM networks for temporal modeling, and an attention mechanism to enhance interpretability and focus on critical gait features. The approach leverages multimodal datasets, including tDCS FOG, DeFOG, Daily Living, and Hantao’s Multimodal, to ensure robustness and generalizability. The proposed model deals with sensor noise, inter-subject variability, and data imbalance through comprehensive preprocessing techniques such as sensor fusion, normalization, and data augmentation. The proposed model achieved an average accuracy of 92.5%, F1-score of 89.3%, and AUC of 0.91, outperforming state-of-the-art methods. Post-training quantization and pruning enabled deployment on edge devices such as Raspberry Pi and Coral TPU, achieving inference latency under 350 ms. Ablation studies show the critical contribution of key architectural components to the model’s effectiveness. Optimized to be deployed real-time, it is a potentially promising solution that can help correctly detect FoG, thereby achieving better clinical monitoring and improving patients’ outcomes in a controlled as well as real world.https://www.frontiersin.org/articles/10.3389/fphys.2025.1581699/fullwearable sensorfreezing of gaitdeep learningattention mechanismartifcial intelligence |
| spellingShingle | Mosleh Hmoud Al-Adhaileh Mosleh Hmoud Al-Adhaileh Asim Wadood Theyazn H. H. Aldhyani Theyazn H. H. Aldhyani Safeer Khan M. Irfan Uddin Abdullah H. Al-Nefaie Abdullah H. Al-Nefaie Deep learning techniques for detecting freezing of gait episodes in Parkinson’s disease using wearable sensors Frontiers in Physiology wearable sensor freezing of gait deep learning attention mechanism artifcial intelligence |
| title | Deep learning techniques for detecting freezing of gait episodes in Parkinson’s disease using wearable sensors |
| title_full | Deep learning techniques for detecting freezing of gait episodes in Parkinson’s disease using wearable sensors |
| title_fullStr | Deep learning techniques for detecting freezing of gait episodes in Parkinson’s disease using wearable sensors |
| title_full_unstemmed | Deep learning techniques for detecting freezing of gait episodes in Parkinson’s disease using wearable sensors |
| title_short | Deep learning techniques for detecting freezing of gait episodes in Parkinson’s disease using wearable sensors |
| title_sort | deep learning techniques for detecting freezing of gait episodes in parkinson s disease using wearable sensors |
| topic | wearable sensor freezing of gait deep learning attention mechanism artifcial intelligence |
| url | https://www.frontiersin.org/articles/10.3389/fphys.2025.1581699/full |
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